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task_categories:
  - question-answering
pretty_name: Every Prompt
size_categories:
  - 1M<n<10M
multilinguality:
  - multilingual

Every Prompt

Every Prompt is a data-driven approach to mining instructions from the web. It contains over a million FAQs and HowTos from around the world in a structured format. It also has basic pre-processing to calculate the length of the useful text and identify the language of that text with the help of GCLD3

It relies on the Web Data Commons dataset (from October 2022) to find the seed list of sites with HowTo and FAQPage items. The general pipeline looks like this:

  • Download 1.6TB of structured data from webdatacommons to identify the pages with the structured data we need (wget/parallel). That gives us 1,985,925 seed pages
  • Crawls the seed pages and tries to extract structured data using extruct package. That left around 1,358,638 pages which are alive and well-formed.
  • Extracts only the relevant structured data of the HowTo/FAQPage type with the help of jmespath. That boils down to 1,266,926 json documents.
  • Extracts the textual information out of the structure to identify the text's language, the textual data's length, and the text/data ratio.

You can use the resulting dataset by filtering for the language and amount of the text. You need to convert the structured data into instructions yourself. You'll need to apply extra cleansing/evaluation of the instructions you've got because, you know, the internet is still full of crap.

Caveat emptor: the format of the FAQs and HowTo's in the dataset might vary greatly. Account for that. To understand potential pitfalls, look at the jmespath expression at the export_structured_data.py.

Detailed stats (with breakdown by language and data type)

language FAQPage count FAQPage text length HowTo count HowTo text length items count text length
en 592730 1186748927 29017 77135350 621747 1263884277
de 83184 213931486 3370 13905977 86554 227837463
es 63237 113906536 6466 30517773 69703 144424309
fr 65081 141638675 3672 21632272 68753 163270947
ja 55439 46231152 1402 1678468 56841 47909620
ru 41271 70947161 2403 12805308 43674 83752469
nl 34066 102719276 2007 11078079 36073 113797355
it 23076 43968063 2465 13696136 25541 57664199
vi 23115 38603954 720 3224051 23835 41828005
zh 22496 21111729 1112 1513344 23608 22625073
pl 19424 41446645 306 419787 19730 41866432
fa 17263 31294557 1819 1915117 19082 33209674
tr 13619 20040069 722 418695 14341 20458764
und 12256 1032156 322 8941 12578 1041097
pt 10784 26163387 1775 8295306 12559 34458693
ro 10536 16405628 75 89946 10611 16495574
id 8256 14353165 1871 13055561 10127 27408726
ko 8348 7624222 616 1533830 8964 9158052
sv 8007 15926376 390 638054 8397 16564430
ar 6950 10240266 1241 7517175 8191 17757441
da 7691 15277244 408 450176 8099 15727420
cs 7546 13201121 480 2471544 8026 15672665
fi 7767 14468764 199 170138 7966 14638902
hi 4517 4307716 683 4294129 5200 8601845
hu 4866 10639836 125 61118 4991 10700954
el 4600 10555382 103 55576 4703 10610958
no 4357 8426887 179 354796 4536 8781683
uk 4401 6925331 90 37285 4491 6962616
iw 4056 7723904 36 35305 4092 7759209
bg 3620 10154727 41 31268 3661 10185995
sk 2639 4394140 65 32527 2704 4426667
th 1877 3823867 613 3171583 2490 6995450
mr 2002 2274197 57 75906 2059 2350103
mt 1886 3761332 14 5443 1900 3766775
cy 1524 3171667 25 11641 1549 3183308
bs 1366 2031881 34 23298 1400 2055179
et 1299 1694117 5 2005 1304 1696122
ms 989 1927545 174 720492 1163 2648037
ca 1068 1614073 62 34072 1130 1648145
lt 1056 2272916 44 57169 1100 2330085
ne 966 771410 29 28569 995 799979
hr 796 1394174 15 10191 811 1404365
fy 743 633705 24 5823 767 639528
lb 703 1133527 18 3985 721 1137512
gl 628 1159618 34 9049 662 1168667
mn 644 1174921 11 3592 655 1178513
la 635 363380 13 2009 648 365389
af 577 444351 38 14403 615 458754
sl 451 1708497 50 50361 501 1758858
ht 455 223768 13 4406 468 228174
lv 317 1017694 32 31983 349 1049677
gd 273 295170 52 20374 325 315544
sr 287 367782 23 5177 310 372959
co 288 284629 12 3530 300 288159
az 268 273548 9 13011 277 286559
fil 210 165520 63 77100 273 242620
jv 244 153411 14 75932 258 229343
sn 239 175459 10 8890 249 184349
bn 190 301199 42 23451 232 324650
ga 198 263174 30 12905 228 276079
mg 201 53082 18 6141 219 59223
hi-Latn 194 250495 4 33091 198 283586
hmn 173 793850 16 5902 189 799752
ka 162 262305 8 3427 170 265732
ig 136 129243 10 2941 146 132184
is 139 236415 4 1277 143 237692
ta 129 155042 12 4079 141 159121
kk 102 152629 28 11885 130 164514
eu 118 130847 10 3522 128 134369
eo 121 69071 6 1885 127 70956
ur 93 259680 33 20499 126 280179
so 112 203877 6 2151 118 206028
tg 99 73437 16 5539 115 78976
mk 29 62730 84 391780 113 454510
be 100 88386 8 2193 108 90579
sm 100 1309239 8 2778 108 1312017
uz 93 116820 7 2987 100 119807
zu 84 136023 9 2744 93 138767
haw 81 59685 6 822 87 60507
sq 74 120593 12 6205 86 126798
ny 78 19403 6 2046 84 21449
hy 66 81675 10 3613 76 85288
ha 44 84457 19 68032 63 152489
ru-Latn 60 40266 1 61 61 40327
el-Latn 57 55657 4 342 61 55999
zh-Latn 58 27522 1 66 59 27588
sd 52 51341 7 2044 59 53385
su 50 17291 7 2358 57 19649
ku 47 23147 6 1998 53 25145
bg-Latn 48 15419 1 414 49 15833
st 25 65162 19 6346 44 71508
yo 37 103685 6 1790 43 105475
ceb 41 72950 1 107 42 73057
ky 30 23062 10 3679 40 26741
te 32 42803 7 2558 39 45361
yi 32 227267 7 2443 39 229710
mi 26 10132 11 2915 37 13047
gu 25 37857 10 4608 35 42465
ja-Latn 33 17560 2 88 35 17648
sw 26 17579 8 2726 34 20305
xh 28 46466 4 1409 32 47875
ml 16 33198 6 2721 22 35919
ps 10 7671 12 2642 22 10313
am 6 8017 8 1987 14 10004
kn 5 22197 9 3523 14 25720
km 7 8936 6 1879 13 10815
pa 10 26617 3 1100 13 27717
si 5 24000 5 1722 10 25722
lo 1 6204 7 2115 8 8319
my 3 14663 3 1179 6 15842

Recreating the results

  1. Clone the repo without the LFS files.
  2. Install requirements from requirements.txt.
  3. Install pv and parallel.
  4. Run bin/get_seed_urls.sh to filter urls of interest out of 1.6TB of compressed data. Don't worry about disk space. Worry about the traffic. That will take around 5h on decent connection.
  5. Run scrapy spider like this scrapy crawl webdatacommons_org -s WEB_DATA_COMMONS=web_data_commons_urls_sample.txt -L INFO -o webdatacommons.jsonlines with WEB_DATA_COMMONS pointing to the list of seed URLs from step 4. That might take up to a few weeks.
  6. Run python bin/extract_relevant_structured_data.py --num-threads 12 webdatacommons.jsonlines relevant.jsonlines.bz2. That's fast, probably around 30 minutes.
  7. Run python bin/export_structured_data.py relevant.jsonlines.bz2 extruct_out.jsonlines.bz2 to obtain the final version of the dataset.
  8. Optionally you can calculate the resulting stats like that: python bin/get_stats.py extruct_out.jsonlines.bz2 every_prompt_stats.csv

Advices

If you want to recreate the results:

  • Get yourself a server or VPS with enough space (80GB should be enough).
  • Look at the code. You'd probably want to make changes here and there.
  • All the python scripts have extra parameters to control the number of threads and the chunk size. Both accept compressed input and output files with the help of smart_open lib.

License

Code of the project has an MIT license.

Copyright: Dmytro Chaplynskyi, lang-uk project, 2023